Scaling Diverse Intermediate Tasks for Zero-Shot Cross-Lingual Transfer in Low-Resource Languages

Zero-shot cross-lingual transfer is a central task in multilingual NLP, allowing models trained in languages with more sufficient training resources to generalize to other low-resource languages. Earlier efforts on this task use parallel corpora, bilingual dictionaries, or other annotated alignment data to improve cross-lingual transferability, which are typically expensive to obtain. In this paper, we propose a simple yet effective method, SALT, to improve the zero-shot cross-lingual transfer of the multilingual pretrained language models without the help of such external data. By incorporati

Research goal: Does scaling the number of diverse intermediate tasks (beyond the nine used in XTREME-R) further improve zero-shot cross-lingual transfer performance for low-resource languages, as measured by F1 score on downstream NLP tasks like XNLI and PAWS-X?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.